Classification of Scoliosis Deformity Three-Dimensional Spinal Shape by Cluster Analysis
Classification of Scoliosis Deformity Three-Dimensional Spinal Shape by Cluster Analysis
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DOI：
10.1097/brs.0b013e318190b914
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发表时间：
2009-03-15
期刊：
影响因子：
3
通讯作者：
Aubin, Carl-Eric
中科院分区：
文献类型：
--
作者：
Stokes, Ian A. F.;Sangole, Archana P.;Aubin, Carl-Eric
研究设计。对现有的脊柱侧弯患者脊柱形态数据库进行聚类分析。目的：确定脊柱侧弯患者是否可以通过三维曲线形状分为不同的组别。可以使用主观或半定量的方法来对脊柱侧弯的曲度类型进行分类，目的是使手术计划合理化。使用聚类分析等客观方法来改进这一过程的报道很少。对1982-1990年间在脊柱侧弯诊所接受脊柱立体摄影的110名患者进行了研究。56人进行了纵向研究(平均每次就诊3.4次)，总共提供了245次观察。选择的患者有2个脊柱侧弯，顶点在T4和L3之间，通过自动测量，两个Cobb角都是9度。根据立体X线片重建脊柱的三维形态。每条曲线通过其Cobb角、顶椎水平、顶椎旋转和最大曲率平面(PMC)旋转(8个变量)进行量化。聚类分析根据这些变量对每次就诊的患者进行分类。当分析搜索4个组时，最大的组(245个观察中的148个)是两个曲线的PMC逆时针旋转的模式(典型地，右上侧凸合并后凸，左下侧弯合并前凸)。其他3组(48、34和15个观察值)是这些变量的其他排列。观察到所有其他变量在组间有很大重叠。在纵向观察的56名患者中，25名患者在所有临床就诊时都一致分组。临床人群中有2个脊柱侧弯的患者的脊柱形态根据PMC在2个曲线区旋转标志的4个排列形成不同的组。这种模式可能会随着重复观察而改变，通常是因为矢状面上的轻微曲率可能会因姿势变化和测量误差而改变。不同组间其他曲线形状变量的重叠表明，这些脊柱畸形分类本身不应决定治疗策略。
Study Design. Cluster analysis of existing database of spinal shape of patients attending a scoliosis clinic.Objective. To determine whether patients with scoliosis can be classified into distinct groups by 3-dimensional curve shape.Summary of Background Data. Subjective or semi-quantitative methods can be used to classify curve types in scoliosis, with the goal of rationalizing surgical planning. There are very few reports of using objective methods such as cluster analysis to improve this process.Methods. One hundred ten patients who underwent radiography of the spine by a stereo technique, at a scoliosis clinic in the period between 1982 and 1990, were studied. Fifty-six were studied longitudinally (average 3.4 clinic visits each), providing 245 total observations. Selected patients had 2 scoliosis curves with apex between T4 and L3, and both Cobb angles >9 degrees by an automated measurement. The 3-dimensional spinal shape was reconstructed from stereo-radiographs. Each curve was quantified by its Cobb angle, apex level, apex vertebra rotation, and rotation of the plane of maximum curvature (PMC) (8 variables). Cluster analysis classified each patient at each visit by these variables.Results. When the analysis searched for 4 clusters, the largest cluster (148 of 245 observations) was the pattern having counterclockwise rotation of the PMC of both curves (typically, a right upper scoliosis curve with kyphosis and left lower scoliosis curve with lordosis). The other 3 clusters (48, 34, and 15 observations) were the other permutations of these variables. Substantial overlap of all the other variables between groups was observed. Of the 56 patients seen longitudinally, 25 were consistently grouped at all clinic visits.Conclusion. Spinal shape of patients in a clinic population with 2 scoliosis curves form distinct groups according to the 4 permutations of the signs of the rotations of the PMC in 2 curve regions. The pattern can change with repeated observation, often because a slight curvature in the sagittal plane can change because of postural variation and measurement errors. Overlap of the other curve-shape variables between groups suggests that these spinal deformity classifications alone should not determine treatment strategy.